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Version: 1.0.0

dtypes

This module provides custom data types for Spotlight dataset.

Classes​

Embedding(data, dtype=None)​

Data sample projected onto a new space.

Attributes​

data : 1-dimensional array-like. Sample embedding.

dtype : Optional data type of embedding. If None, data type inferred from data.

Example​

>>> import numpy as np
>>> from renumics.spotlight import Dataset, Embedding
>>> value = np.array(np.random.rand(2))
>>> embedding = Embedding(value)
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_embedding_column("embeddings", 5*[embedding])
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(len(dataset["embeddings", 3].data))
2

Ancestors (in MRO)​

  • renumics.spotlight.dtypes._BaseData
  • abc.ABC

Static methods​

decode(value)​

Restore class from its numpy representation.

Methods​

encode(self)​

Convert to numpy for storing in dataset.

Args​

target : Optional target format.

Sequence1D(index, value=None, dtype=None)​

One-dimensional ndarray with optional index values.

Attributes​

index : 1-dimensional array-like of length num_steps. Index values (x-axis).

value : 1-dimensional array-like of length num_steps. Respective values (y-axis).

dtype : Optional data type of sequence. If None, data type inferred from data.

Example​

>>> import numpy as np
>>> from renumics.spotlight import Dataset, Sequence1D
>>> index = np.arange(100)
>>> value = np.array(np.random.rand(100))
>>> sequence = Sequence1D(index, value)
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_sequence_1d_column("sequences", 5*[sequence])
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(len(dataset["sequences", 2].value))
100

Ancestors (in MRO)​

  • renumics.spotlight.dtypes._BaseData
  • abc.ABC

Static methods​

decode(value)​

Restore class from its numpy representation.

empty()​

Create an empty sequence.

Methods​

encode(self)​

Convert to numpy for storing in dataset.

Args​

target : Optional target format.

Mesh(points, triangles, point_attributes=None, triangle_attributes=None, point_displacements=None)​

Triangular 3D mesh with optional per-point and per-triangle attributes and optional per-point displacements over time.

Example​

>>> import numpy as np
>>> from renumics.spotlight import Dataset, Mesh
>>> points = np.array([[0,0,0],[1,1,1],[0,1,0],[-1,0,1]])
>>> triangles = np.array([[0,1,2],[2,3,0]])
>>> mesh = Mesh(points, triangles)
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_mesh_column("meshes", 5*[mesh])
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(dataset["meshes", 2].triangles)
[[0 1 2]
 [2 3 0]]

Ancestors (in MRO)​

  • renumics.spotlight.dtypes._BaseFileBasedData
  • renumics.spotlight.dtypes._BaseData
  • abc.ABC

Static methods​

decode(value)​

Restore class from its numpy representation.

empty()​

Create an empty mesh.

from_file(filepath)​

Read mesh from a filepath or an URL.

trimesh is used inside, so only supported formats are allowed.

from_trimesh(mesh)​

Import a trimesh.Trimesh mesh.

Instance variables​

point_attributes Mapping str -> :code:np.array with shape (num_points, ...). Point-wise attributes corresponding to points. All possible shapes of a single attribute can be found in renumics.spotlight.mesh_proc.gltf.GLTF_SHAPES.

point_displacements List of arrays with shape (num_points, 3). Point-wise relative displacements (offsets) over the time corresponding to points. Timestep 0 is omitted since it is explicit stored as absolute values in points.

points :code:np.array with shape (num_points, 3). Mesh points.

triangles :code:np.array with shape (num_triangles, 3). Mesh triangles stored as their CCW nodes referring to the points indices.

Methods​

encode(self)​

Convert to numpy for storing in dataset.

Args​

target : Optional target format.

interpolate_point_displacements(self, num_timesteps)​

subsample time dependent attributes with new time step count

update_attributes(self, point_attributes=None, triangle_attributes=None)​

Update point and/or triangle attributes dict-like.

Image(data)​

An RGB(A) or grayscale image that will be saved in encoded form.

Attributes​

data : Array-like with shape (num_rows, num_columns) or (num_rows, num_columns, num_channels) with num_channels equal to 3, or 4; with dtype "uint8".

Example​

>>> import numpy as np
>>> from renumics.spotlight import Dataset, Image
>>> data = np.full([100,100,3], 255, dtype=np.uint8)  # white uint8 image
>>> image = Image(data)
>>> float_data = np.random.uniform(0, 1, (100, 100))  # random grayscale float image
>>> float_image = Image(float_data)
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_image_column("images", [image, float_image, data, float_data])
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(dataset["images", 0].data[50][50])
...     print(dataset["images", 3].data.dtype)
[255 255 255]
uint8

Ancestors (in MRO)​

  • renumics.spotlight.dtypes._BaseFileBasedData
  • renumics.spotlight.dtypes._BaseData
  • abc.ABC

Static methods​

decode(value)​

Restore class from its numpy representation.

empty()​

Create a transparent 1 x 1 image.

from_file(filepath)​

Read image from a filepath, an URL, or a file-like object.

imageio is used inside, so only supported formats are allowed.

Methods​

encode(self)​

Convert to numpy for storing in dataset.

Args​

target : Optional target format.

Audio(sampling_rate, data)​

An Audio Signal that will be saved in encoded form.

All formats and codecs supported by AV are supported for read.

Attributes​

data : Array-like with shape (num_samples, num_channels) with num_channels <= 5. If data has a float dtype, its values should be between -1 and 1. If data has an int dtype, its values should be between minimum and maximum possible values for the particular int dtype. If data has an unsigned int dtype, ist values should be between 0 and maximum possible values for the particular unsigned int dtype.

sampling_rate : Sampling rate (samples per seconds)

Example​

>>> import numpy as np
>>> from renumics.spotlight import Dataset, Audio
>>> samplerate = 44100
>>> fs = 100 # 100 Hz audio signal
>>> time = np.linspace(0.0, 1.0, samplerate)
>>> amplitude = np.iinfo(np.int16).max * 0.4
>>> data = np.array(amplitude * np.sin(2.0 * np.pi * fs * time), dtype=np.int16)
>>> audio = Audio(samplerate, np.array([data, data]).T)  # int16 stereo signal
>>> float_data = 0.5 * np.cos(2.0 * np.pi * fs * time).astype(np.float32)
>>> float_audio = Audio(samplerate, float_data)  # float32 mono signal
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_audio_column("audio", [audio, float_audio])
...     dataset.append_audio_column("lossy_audio", [audio, float_audio], lossy=True)
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(dataset["audio", 0].data[100])
...     print(f"{dataset['lossy_audio', 1].data[0, 0]:.5g}")
[12967 12967]
0.4596

Ancestors (in MRO)​

  • renumics.spotlight.dtypes._BaseFileBasedData
  • renumics.spotlight.dtypes._BaseData
  • abc.ABC

Static methods​

decode(value)​

Restore class from its numpy representation.

empty()​

Create a single zero-value sample stereo audio signal.

from_file(filepath)​

Read audio file from a filepath, an URL, or a file-like object.

pyav is used inside, so only supported formats are allowed.

get_format_codec(target=None)​

Get an audio format and an audio codec by an target.

Methods​

encode(self, target=None)​

Convert to numpy for storing in dataset.

Args​

target : Optional target format.

Category(...)​

A string value that takes only a limited number of possible values (categories).

The corresponding categories can be got and set with get/set_column_attributes['categories'].

Dummy class for window column creation, should not be explicitly used as input data.

Example​

>>> import numpy as np
>>> from renumics.spotlight import Dataset
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_categorical_column("my_new_cat",
...         categories=["red", "green", "blue"],)
...     dataset.append_row(my_new_cat="blue")
...     dataset.append_row(my_new_cat="green")
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(dataset["my_new_cat", 1])
green

Example​

>>> import numpy as np
>>> import datetime
>>> from renumics.spotlight import Dataset
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_categorical_column("my_new_cat",
...         categories=["red", "green", "blue"],)
...     current_categories = dataset.get_column_attributes("my_new_cat")["categories"]
...     dataset.set_column_attributes("my_new_cat", categories={**current_categories,
...         "black":100})
...     dataset.append_row(my_new_cat="black")
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(dataset["my_new_cat", 0])
black

Ancestors (in MRO)​

  • builtins.str

Video(data)​

A video object. No encoding or decoding is currently performed on the python side, so all formats will be saved into dataset without compatibility check, but only the formats supported by your browser (apparently .mp4, .ogg, .webm, .mov etc.) can be played in Spotlight.

Ancestors (in MRO)​

  • renumics.spotlight.dtypes._BaseFileBasedData
  • renumics.spotlight.dtypes._BaseData
  • abc.ABC

Static methods​

decode(value)​

Restore class from its numpy representation.

empty()​

Create an empty video instance.

from_file(filepath)​

Read video from a filepath or an URL.

Methods​

encode(self)​

Convert to numpy for storing in dataset.

Args​

target : Optional target format.

Window()​

A pair of two timestamps in seconds which can be later projected onto continuous data (only :class:Audio <renumics.spotlight.dtypes.Audio> is currently supported).

Dummy class for window column creation (see :func:Dataset.append_column <renumics.spotlight.dataset.Dataset.append_column>), should not be explicitly used as input data.

To create a window column, use :func:Dataset.append_window_column <renumics.spotlight.dataset.Dataset.append_window_column> method.

Examples​

>>> import numpy as np
>>> from renumics.spotlight import Dataset
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_window_column("window", [[1, 2]] * 4)
...     dataset.append_row(window=(0, 1))
...     dataset.append_row(window=np.array([-1, 0]))
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(dataset["window"])
[[ 1.  2.]
 [ 1.  2.]
 [ 1.  2.]
 [ 1.  2.]
 [ 0.  1.]
 [-1.  0.]]
>>> import numpy as np
>>> from renumics.spotlight import Dataset
>>> with Dataset("docs/example.h5", "w") as dataset:
...     dataset.append_int_column("start", range(5))
...     dataset.append_float_column("end", dataset["start"] + 2)
...     print(dataset["start"])
...     print(dataset["end"])
[0 1 2 3 4]
[2. 3. 4. 5. 6.]
>>> with Dataset("docs/example.h5", "a") as dataset:
...     dataset.append_window_column("window", zip(dataset["start"], dataset["end"]))
>>> with Dataset("docs/example.h5", "r") as dataset:
...     print(dataset["window"])
[[0. 2.]
 [1. 3.]
 [2. 4.]
 [3. 5.]
 [4. 6.]]